An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids

Electricity theft can be considered as a Nontechnical Loss (NTL) in smart grids, which is very harmful to the power system. Electricity Theft Detection (ETD) is a procedure to detect atypical behaviours in smart grids, which can be achieved via the massive amount of data that is generated by these networks due to using smart meter tools and Information and Communications Technology (ICT). Since the existing methods are not exceptionally robust to detect this type of attack, also considering the strength of the convolutional neural network (CNN), an Ensemble Deep Convolutional Neural Network (EDCNN) algorithm for ETD in smart grids has been proposed. As the first layer of the model, a random under bagging technique is applied to deal with the imbalance data, then deep CNNs are utilized on each subset, and finally, a voting system is embedded as the last part. This study has been conducted on a dataset which contains consumption information of more than 42,000 customers over 24 months. Various performance parameters containing AUC, precision, recall, f1-score and accuracy have been reported as the results.